├── PHM Jinan ├── 对比1 │ ├── AStar.m │ ├── AStar_referance.m │ ├── Correction.m │ ├── FindList.m │ ├── GetBoundary.m │ ├── GetObstacle.m │ ├── GetPath.m │ ├── Interpolation_Spline0.m │ ├── MotionModel.m │ ├── compute_Attract.m │ ├── compute_Attract1.m │ ├── compute_angle.m │ ├── compute_angle_test.m │ ├── compute_repulsion.m │ ├── cubic.m │ ├── deal_line_obstacle.m │ ├── detect_obstacle.m │ ├── h.m │ ├── isObstacle.m │ ├── isSamePosi.m │ ├── is_same_num.m │ ├── isopen.m │ ├── search_B.m │ ├── search_B_main.m │ ├── solve_line_circle.m │ └── test_if_pass_obstacle.m ├── 对比2 │ ├── AStar.m │ ├── AStar_referance.m │ ├── Astar_update_main.m │ ├── Correction.m │ ├── FindList.m │ ├── GetBoundary.m │ ├── GetObstacle.m │ ├── GetPath.m │ ├── Interpolation_Spline0.m │ ├── MotionModel.m │ ├── compute_Attract.m │ ├── compute_Attract1.m │ ├── compute_angle.m │ ├── compute_angle_test.m │ ├── compute_repulsion.m │ ├── cubic.m │ ├── deal_line_obstacle.m │ ├── detect_obstacle.m │ ├── h.m │ ├── isObstacle.m │ ├── isSamePosi.m │ ├── is_same_num.m │ ├── isopen.m │ ├── solve_line_circle.m │ └── test_if_pass_obstacle.m └── 对比4 │ ├── AStar.m │ ├── AStar_referance.m │ ├── Astar_update_main.m │ ├── Correction.m │ ├── FindList.m │ ├── GetBoundary.m │ ├── GetObstacle.m │ ├── GetPath.m │ ├── Interpolation_Spline0.m │ ├── MotionModel.m │ ├── compute_Attract.m │ ├── compute_Attract1.m │ ├── compute_angle.m │ ├── compute_angle_test.m │ ├── compute_repulsion.m │ ├── cubic.m │ ├── deal_line_obstacle.m │ ├── detect_obstacle.m │ ├── h.m │ ├── isObstacle.m │ ├── isSamePosi.m │ ├── is_same_num.m │ ├── isopen.m │ ├── solve_line_circle.m │ ├── test_if_pass_obstacle.m │ └── 改进A_算法在AGV小车路径规划中的应用_王立春.pdf ├── PHM Shanghai ├── Path Planning Using Artificial Potential Field Method.pdf ├── p21-Ju.pdf ├── 参考文献1 │ ├── AStar.m │ ├── Correction.m │ ├── FindList.m │ ├── GetBoundary.m │ ├── GetObstacle.m │ ├── GetPath.m │ ├── Interpolation_Spline0.m │ ├── MotionModel.m │ ├── SplineThree.m │ ├── arterfical.m │ ├── arterfical_correction.m │ ├── compute_Attract.m │ ├── compute_Attract1.m │ ├── compute_angle.m │ ├── compute_angle_test.m │ ├── compute_repulsion.m │ ├── cubic.m │ ├── h.m │ ├── isObstacle.m │ ├── isSamePosi.m │ ├── isopen.m │ └── 基于改进A_算法和人工势场法的搬运机器人路径规划研究_蔺博见.caj └── 参考文献2 │ ├── AStar.m │ ├── Correction.m │ ├── FindList.m │ ├── GetBoundary.m │ ├── GetObstacle.m │ ├── GetPath.m │ ├── Interpolation_Spline0.m │ ├── MotionModel.m │ ├── SplineThree.m │ ├── arterfical.m │ ├── arterfical_correction.m │ ├── compute_Attract.m │ ├── compute_Attract1.m │ ├── compute_angle.m │ ├── compute_angle_test.m │ ├── compute_repulsion.m │ ├── cubic.m │ ├── h.m │ ├── isObstacle.m │ ├── isSamePosi.m │ ├── isopen.m │ ├── main_prcedure.m │ └── 基于改进A_算法和人工势场法智能小车路径规划研究_宣仁虎.caj ├── PHM济南 └── 对比1 │ └── Astar_update_main.m ├── README.md ├── 上海PHM └── 参考文献1 │ └── main_prcedure.m └── 升级后的改进A星算法 ├── 与传统A星对比 ├── ASTARPATH.m ├── ASTARPATH_improved.m ├── ExampleOnTheUseOfAStarAlgorithm.m ├── deal_line_obstacle.m ├── detect_obstacle.m ├── solve_line_circle.m ├── test_if_pass_obstacle.m └── 与TA对比.jpg ├── 基于改进A_算法的仓储物流移动机器人任务调度和路径优化研究_王秀红 └── 对比1 │ ├── ASTARPATH.asv │ ├── ASTARPATH.m │ ├── ASTARPATH_improved.m │ ├── Connecting=1.fig │ ├── ExampleOnTheUseOfAStarAlgorithm.m │ ├── deal_line_obstacle.m │ ├── detect_obstacle.m │ ├── solve_line_circle.m │ ├── test_if_pass_obstacle.m │ └── 基于改进A_算法的仓储物流移动机器人任务调度和路径优化研究_王秀红.pdf ├── 寻优 ├── 对B寻优 │ ├── ASTARPATH.m │ ├── ASTARPATH_improved.m │ ├── ExampleOnTheUseOfAStarAlgorithm.m │ ├── deal_line_obstacle.m │ ├── detect_obstacle.m │ ├── search_B.m │ ├── search_B_main.m │ ├── solve_line_circle.m │ ├── test_if_pass_obstacle.m │ ├── 与TA对比.jpg │ └── 评估.m ├── 对connect_distance寻优 │ ├── ASTARPATH.m │ ├── ASTARPATH_improved.m │ ├── ExampleOnTheUseOfAStarAlgorithm.asv │ ├── ExampleOnTheUseOfAStarAlgorithm.m │ ├── deal_line_obstacle.m │ ├── detect_obstacle.m │ ├── search_Connecting_Distance.m │ ├── search_Connecting_Distance_main.m │ ├── solve_line_circle.m │ ├── test_if_pass_obstacle.m │ ├── 与TA对比.jpg │ └── 评估.m ├── 对r进行寻优 │ ├── ASTARPATH.m │ ├── ASTARPATH_improved.m │ ├── ExampleOnTheUseOfAStarAlgorithm.m │ ├── deal_line_obstacle.m │ ├── detect_obstacle.m │ ├── search_r.m │ ├── search_r_main.m │ ├── solve_line_circle.m │ ├── test_if_pass_obstacle.m │ └── 评估.m └── 寻优结果.docx ├── 改进A_算法在AGV小车路径规划中的应用_王立春 └── 对比2 │ ├── ASTARPATH.m │ ├── ASTARPATH_improved.m │ ├── ExampleOnTheUseOfAStarAlgorithm.m │ ├── deal_line_obstacle.m │ ├── detect_obstacle.m │ ├── solve_line_circle.m │ ├── test_if_pass_obstacle.m │ └── 改进A_算法在AGV小车路径规划中的应用_王立春.pdf └── 数据 ├── 对B寻优数据结果 ├── 1.txt ├── B寻优.jpg └── IM_B_LENGTH.txt ├── 对r寻优数据结果 ├── 1.txt ├── IM_R_LENGTH.txt ├── R.jpg └── 升级.jpg ├── 对search_Connecting_Distance寻优数据 ├── Connece_Distance=1.jpg ├── Connece_Distance=2.jpg ├── Connece_Distance_length.jpg └── IM_Connecting_Distance_LENGTH.txt └── 对比 ├── 与传统A星对比 └── TA对比.jpg ├── 几种方法对比.xlsx ├── 基于改进A_算法的仓储物流移动机器人任务调度和路径优化研究_王秀红 └── resultq.fig └── 改进A_算法在AGV小车路径规划中的应用_王立春 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| p.goal=[40 40]; %目标点 8 | p.XYMAX=40; %%代表我们要画一个地图的长和宽 9 | obstacle=GetBoundary(p);%得到边界数据 10 | nObstacle=100;%在地图中随机加入20个障碍物 11 | obstacle=GetObstacle(nObstacle,p)%障碍物和边界 12 | %终点设置为（40,40）时间减少了很多 但是路径长度小的 13 | obstacle=[20,11;20,22;35,39;14,29;18,34;39,17;2,19;39,22;8,8;27,30;23,20;27,26;14,12;25,6;32,19;1,14;3,32;39,31;26,27;9,5;16,1;5,22;11,11;10,38;13,39;6,11;20,20;5,36;35,24;4,35;37,38;16,22;2,29;14,23;29,1;32,18;22,26;27,21;36,15;2,37;12,33;2,34;8,15;29,24;29,35;35,37;23,27;3,8;37,26;32,3;28,27;28,29;25,26;25,24;21,20;;15,16;15,14;] 14 | %终点设置为（20,20） 但是时间加长了 ，路径长度短了 15 | %obstacle=[19,2;20,18;1,10;10,12;17,6;5,2;17,17;16,3;13,7;0,12;8,20;18,10;14,20;8,5;13,8;5,14;11,1;20,15;17,8;6,16;14,8;7,6;2,14;5,8;16,2;2,15;11,13;16,1;15,2;3,20;9,10;7,0;10,1;12,3;3,18;18,9;13,11;3,10;11,1;19,18;16,6;14,5;9,12;5,10;11,7;5,13;6,19;18,12;18,6;16,4;19,9;4,1;10,18;13,12;18,4;13,10;8,1;15,17;16,9;12,11;2,11;18,14;1,7;11,2;2,9;8,1;16,15;5,1;18,19;14,15;8,19;5,10;5,5;15,5;1,15;14,20;12,7;15,15;20,2;8,12;5,9;18,15;16,5;9,2;2,9;11,13;4,3;11,9;7,13;6,17;15,6;10,8;16,18;4,14;14,5;1,15;16,6;14,6;19,0] 16 | [n0 n1]=size(obstacle); 17 | 18 | %%%%%改进前根据论文’改进A_算法的AGV路径规划_刘生伟‘改进A*算法主程序A*算法************ 19 | t1=clock; 20 | [ path3 STEP_a_time ]=AStar_referance(obstacle,p,1);%A*算法 21 | t2=clock; 22 | TA_time=etime(t2,t1); 23 | 24 | [x g]=size(path3); 25 | % %%冗余段测试程序 26 | % % obstacle=[2.5,5;6,12;8,16] 27 | % % path3=[1,2;2,4;3,6;4,8;5,10;9,18;10,20;11,22]; 28 | % % 29 | % % TA_time=etime(t2,t1); 30 | % %%%%%% 31 | % col_num=[]; 32 | % k=1; 33 | % for i=1:1:x 34 | % for j=(k+2):1:x 35 | % [value]=detect_obstacle(path3(k,:),path3(j,:),obstacle) 36 | % if value==0 37 | % %%%记录要删掉的行号 38 | % c=j-1; 39 | % col_num=[col_num,c]; 40 | % else 41 | % k=j 42 | % break; 43 | % end 44 | % end 45 | % % if j==x 46 | % % break; 47 | % % end 48 | % end 49 | % %%%%根据col_num删除冗余的行数 50 | % % d=col_num; 51 | % % b=cumsum(path); 52 | % col_num=unique(col_num','rows'); 53 | % d=col_num'; 54 | % [n11,n12]=size(path3) 55 | % path2=[]; 56 | % for i=1:1:n11 57 | % [a]=is_same_num(i,d') 58 | % if a==0 59 | % path2=[path2;path3(i,:)]; 60 | % end 61 | % end 62 | % path3=path2; 63 | % 64 | % 65 | % 66 | 67 | plot(p.start(1),p.start(2),'*r','LineWidth',2);hold on; 68 | plot(p.goal(1),p.goal(2),'*b','LineWidth',2);hold on; 69 | %title('Comparison of A star algorithm before and after improvement'); 70 | if length(path3)>=1 71 | plot(path3(:,1),path3(:,2),'-r','LineWidth',2);hold on; 72 | end 73 | hold on 74 | 75 | 76 | 77 | %%%%*****验证该直线穿过的障碍物的点%% 78 | 79 | t3=clock; 80 | [line_obstacle]=test_if_pass_obstacle(p,obstacle); 81 | t8=clock; 82 | A_time_test1=etime(t8,t3); 83 | 84 | %计算线段上障碍物的起点终点处理 85 | r=0.5; 86 | [path_line_start_ago path_line_goal_ago]=solve_line_circle(r,line_obstacle,p) 87 | t9=clock; 88 | %%%对生成后的局域起点和终点进行合并 89 | 90 | path_line_start=path_line_start_ago; 91 | path_line_goal=path_line_goal_ago; 92 | [N9 N10]=size(path_line_start) 93 | new_start_x=[]; 94 | new_start_y=[]; 95 | new_goal_x=[]; 96 | new_goal_y=[]; 97 | N=1; 98 | % if N9==1 99 | % return; 100 | for i=1:1:N9-1 101 | if N9==1 102 | break; 103 | end 104 | if path_line_goal(i,1)==path_line_start(i+1,1)&&path_line_goal(i,2)==path_line_start(i+1,2) 105 | %先存好 106 | new_start_x=[new_start_x;path_line_start(i,1)]; 107 | new_start_y=[new_start_y;path_line_start(i,2)]; 108 | new_goal_x=[new_goal_x;path_line_goal(i+1,1)]; 109 | new_goal_y=[new_goal_y;path_line_goal(i+1,2)]; 110 | %修改原寄存器方便后续的处理 111 | path_line_start(i+1,1)=path_line_start(i,1); 112 | elseif i==N9-1 113 | new_start_x=[new_start_x;path_line_start(i,1);path_line_start(i+1,1)]; 114 | new_start_y=[new_start_y;path_line_start(i,2);path_line_start(i+1,2)]; 115 | new_goal_x=[new_goal_x;path_line_goal(i,1);path_line_goal(i+1,1)]; 116 | new_goal_y=[new_goal_y;path_line_goal(i,2);path_line_goal(i+1,2)]; 117 | else 118 | new_start_x=[new_start_x;path_line_start(i,1)]; 119 | new_start_y=[new_start_y;path_line_start(i,2)]; 120 | new_goal_x=[new_goal_x;path_line_goal(i,1)]; 121 | new_goal_y=[new_goal_y;path_line_goal(i,2)]; 122 | end 123 | end 124 | %%检测new是否需要处理 125 | [N11 N12]=size(new_start_x) 126 | if N11>0 127 | path_line_start_ago=[new_start_x,new_start_y]; 128 | path_line_goal_ago=[new_goal_x,new_goal_y] 129 | end 130 | 131 | [Goal STEP_TIME]=deal_line_obstacle(obstacle,path_line_start_ago,path_line_goal_ago,p,value_B) 132 | t10=clock; 133 | A_time=etime(t10,t9); 134 | % path=AStar(obstacle,p);%A*算法 135 | Goal1=unique(Goal,'rows'); 136 | 137 | 138 | t4=clock; 139 | A_time=etime(t4,t3); 140 | X=Goal1(:,1); 141 | Y=Goal1(:,2); 142 | m=[X]; 143 | N=[Y]; 144 | plot(m,N,'b','LineWidth',2) 145 | hold on 146 | %绘制地图障碍物起点终点 147 | plot(obstacle(:,1),obstacle(:,2),'o')%,p.goal(1),p.goal(2),'v',p.start(1),p.start(2),'ms',X,Y,'.r'); 148 | % figure(1) 149 | % legend('预设定起点','预设定终点','A*算法改进前轨迹','A*算法改进后算法','障碍物'); 150 | legend('Starting point','Terminal point','Traditional A-star algorithm','Improved A-star algorithm','Obstacles'); 151 | 152 | 153 | FontSize = 15; 154 | set(gca,'FontSize',FontSize); 155 | xlabel('x axis') 156 | ylabel('y axis') 157 | 158 | 159 | 160 | %计算改进前A*法路径长度 161 | N2=size(path3) 162 | TA_length=0; 163 | for i=1:1:(N2-1) 164 | TA_length=TA_length+((path3(i,1)-path3(i+1,1))^2+(path3(i,2)-path3(i+1,2))^2)^(1/2); 165 | end 166 | disp(['A*算法改进前的长度',TA_length]) 167 | 168 | %融合算法的路径长度 169 | N3=size(X) 170 | A_length=0; 171 | for i=1:1:(N3-1) 172 | A_length=A_length+((X(i,1)-X(i+1,1))^2+(Y(i,1)-Y(i+1,1))^2)^(1/2); 173 | end 174 | %disp(['A*算法改后的长度',A_length]) 175 | improved_length=vpa(A_length) 176 | end 177 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Path-Planning-Algorithm 2 | My IEEE Paper published Matlab Code 3 | #PHM-Shnaghai 4 | Path Planning Using Artificial Potential Field Method 5 | And A-star Fusion Algorithm 下载链接https://ieeexplore.ieee.org/document/9280929 6 | #PHM-Jinan 7 | Path Planning Using an Improved A-star Algorithm 8 | 下载链接https://ieeexplore.ieee.org/document/9296641 9 | -------------------------------------------------------------------------------- /上海PHM/参考文献1/main_prcedure.m: -------------------------------------------------------------------------------- 1 | %Author:Jack Ju 2 | %********根据参考文献基于改进A_算法和人工势场法的搬运机器人路径规划研究_蔺博 主程序**********% 3 | clear all; 4 | %障碍和目标，起始位置都已知的路径规划,意图实现从起点可以规划出一条避开障碍到达目标的路径。 5 | %初始化车的参数 6 | %Xo=[0 0];%起点位置 7 | k=40;%计算引力需要的增益系数15 8 | K=0;%初始化 9 | m=5;%计算斥力的增益系数，都是自己设定的5。 10 | Po=2;%障碍影响距离，当障碍和车的距离大于这个距离时，斥力为0，即不受该障碍的影响。也是自己设定。 11 | %碍物的个数 12 | w=0; 13 | n=50;%障碍个数 需要你修改 14 | a=0.5; 15 | l=0.01;%步长 16 | J=3000;%循环迭代次数 17 | %如果不能实现预期目标，可能也与初始的增益系数，Po设置的不合适有关。 18 | %end 19 | %给出障碍和目标信息 20 | p.start=[1 1]; %起始点 21 | p.goal=[11 11]; %目标点 22 | start=p.start; 23 | goal=p.goal; 24 | p.XYMAX=15; %%代表我们要画一个地图的长和宽 25 | %obstacle=GetBoundary(p);%得到边界数据 26 | nObstacle=n;%在地图中随机加入n个障碍物 27 | obstacle=GetObstacle(nObstacle,p)%障碍物和边界 28 | obstacle=[10,14;6,8;12,8;8,3;5,7;14,9;13,10;8,6;9,6;9,15;3,1;5,13;7,14;3,12;13,1;3,4;3,5;3,10;3,2;7,11;5,2;14,10;6,7;3,12;14,11;15,14;7,13;2,5;4,10;6,3;9,0;4,11;9,8;11,7;3,14;2,9;4,9;5,13;6,12;8,9;1,3;4,4;12,13;0,0;14,7;11,3;7,15;9,11;4,8;7,7] 29 | [n0 n1]=size(obstacle); 30 | %******************调用A*算法备份*********% 31 | t3=clock; 32 | path1=AStar(obstacle,p);%A*算法 33 | t4=clock; 34 | A_time=etime(t4,t3) 35 | [x g]=size(path1); 36 | if length(path1)>=1 37 | % plot(path1(:,1),path1(:,2),'-+r','LineWidth',2);hold on; 38 | end 39 | %%%%检测生成路径的拐点******* 40 | %%%%%%具体实现通过改点前后斜率不同判断 41 | guaidian_x=[]; 42 | guaidian_y=[]; 43 | for i=2:1:x-1 44 | jude1=(path1(i,2)-path1(i-1,2))/(path1(i,1)-path1(i-1,1)); 45 | jude2=(path1(i+1,2)-path1(i,2))/(path1(i+1,1)-path1(i,1)); 46 | if jude1~=jude2 47 | 48 | guaidian_x=[guaidian_x;path1(i,1)]; 49 | guaidian_y=[guaidian_y;path1(i,2)]; 50 | end 51 | end 52 | % guaidian_x=sort(guaidian_x); 53 | % guaidian_y=sort(guaidian_y); 54 | guaidian=[guaidian_x,guaidian_y]; 55 | guaidian=[p.start;guaidian;p.goal] 56 | guaidian=unique(guaidian,'rows') 57 | %legend('A*算法轨迹'); 58 | % %**********基于改进A_算法和人工势场法的搬运机器人路径规划研究_蔺博见下人工势场法*******************……% 59 | t1=clock; 60 | [x_num y_num]=size(guaidian); 61 | path_combine=[]; 62 | for i=1:1:x_num-1 63 | p.start=guaidian(i,:); 64 | p.goal=guaidian(i+1,:); 65 | Xsum_orgin=[p.goal;obstacle];%[10 10;1 1.2;3 2.5;4 4.5;3 6;6 2;5.5 5.5;8 8.5];%这个向量是(n+1)*2维，其中[10 10]是目标位置，剩下的都是障碍的位置。 66 | [Goal_orgin time_orgin]=arterfical(k,K,m,Po,w,n,a,l,J,p.start,p.goal,Xsum_orgin,n0,obstacle) 67 | % [Goal time]=arterfical_correction(k,K,m,Po,w,n,a,l,J,p.start,p.goal,Xsum,path1,n0,x,obstacle); 68 | path_combine=[path_combine;Goal_orgin] 69 | end 70 | X1=path_combine(:,1); 71 | Y1=path_combine(:,2); 72 | %计算时间 73 | t2=clock; 74 | art_time=etime(t2,t3) 75 | m1=[X1]; 76 | N1=[Y1]; 77 | plot(m1,N1,'g','LineWidth',2) 78 | % legend('人工势场法'); 79 | hold on 80 | % hold on 81 | %plot(obstacle(:,1),obstacle(:,2),'o')%,p.goal(1),p.goal(2),'v',p.start(1),p.start(2),'ms',X,Y,'.r'); 82 | % legend('预设定起点','预设定终点','A*算法轨迹','改进后融合算法','障碍物'); 83 | % hold on 84 | % title("人工势场法和A*融合算法，加入50个障碍物仿真结果") 85 | 86 | 87 | 88 | 89 | 90 | 91 | 92 | 93 | %******调用改进后的融合算法（A*和人工势场的融合算法）并绘制 94 | t5=clock; 95 | path_fusion=[]; 96 | %%%%改进 97 | % for i=1:1:x_num-1 98 | % p.start=guaidian(i,:); 99 | % p.goal=guaidian(i+1,:); 100 | % Xsum=[p.goal;obstacle;path1];%[10 10;1 1.2;3 2.5;4 4.5;3 6;6 2;5.5 5.5;8 8.5];%这个向量是(n+1)*2维，其中[10 10]是目标位置，剩下的都是障碍的位置。 101 | % [Goal time]=arterfical_correction(k,K,m,Po,w,n,a,l,J,p.start,p.goal,Xsum,path1,n0,x,obstacle); 102 | % path_fusion=[path_fusion;Goal] 103 | % end 104 | % %%%开始绘制改进后的图像 105 | % X=path_fusion(:,1); 106 | % Y=path_fusion(:,2); 107 | Xsum=[goal;obstacle;path1];%[10 10;1 1.2;3 2.5;4 4.5;3 6;6 2;5.5 5.5;8 8.5];%这个向量是(n+1)*2维，其中[10 10]是目标位置，剩下的都是障碍的位置。 108 | [Goal time]=arterfical_correction(k,K,m,Po,w,n,a,l,J,start,goal,Xsum,path1,n0,x,obstacle); 109 | %%%开始绘制改进后的图像 110 | X=Goal(:,1); 111 | Y=Goal(:,2); 112 | t6=clock; 113 | fusion_time=etime(t6,t5)+A_time 114 | m=[X]; 115 | N=[Y]; 116 | plot(m,N,'-b','LineWidth',2) 117 | %legend('改进后融合算法'); 118 | %legend('Fusion algorithm'); 119 | hold on 120 | plot(obstacle(:,1),obstacle(:,2),'o')%,p.goal(1),p.goal(2),'v',p.start(1),p.start(2),'ms',X,Y,'.r'); 121 | %legend('人工势场法','预设定起点','预设定终点','A*算法轨迹','改进后融合算法','障碍物'); 122 | plot(start(1),start(2),'*r','LineWidth',2);hold on; 123 | plot(goal(1),goal(2),'*b','LineWidth',2);hold on; 124 | 125 | legend('Reference method [6] ','Fusion algorithm proposed in this paper','Obstacles','Starting point','Terminal point'); 126 | hold on 127 | %title("人工势场法和A*融合算法，加入50个障碍物仿真结果") 128 | % title("Artificial potential field method and A-star fusion algorithm") 129 | FontSize = 25; 130 | set(gca,'FontSize',FontSize); 131 | xlabel('x axis') 132 | ylabel('y axis') 133 | 134 | %%%%%本部分要计算生成的路径长度 135 | %计算人工势场法路径长度 136 | N1=size(X1) 137 | art_length=0; 138 | for i=1:1:(N1-1) 139 | art_length=art_length+((X1(i,1)-X1(i+1,1))^2+(Y1(i,1)-Y1(i+1,1))^2)^(1/2); 140 | end 141 | disp(['参考文献人工势场法的长度',art_length]) 142 | %计算A*法路径长度 143 | N2=size(path1) 144 | A_length=0; 145 | for i=1:1:(N2-1) 146 | A_length=A_length+((path1(i,1)-path1(i+1,1))^2+(path1(i,2)-path1(i+1,2))^2)^(1/2); 147 | end 148 | disp(['A*算法的长度',A_length]) 149 | 150 | %融合算法的路径长度 151 | N3=size(X) 152 | Fusion_length=0; 153 | for i=1:1:(N3-1) 154 | 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114.33 38 | 114.33 39 | 114.33 40 | 114.33 41 | 114.33 42 | 114.33 43 | 114.33 44 | 114.33 45 | 114.33 46 | 114.33 47 | 114.33 48 | 114.33 49 | 114.33 50 | 114.33 51 | 52 | ]; 53 | x=[]; 54 | for i=1:1:50 55 | x=[x i]; 56 | end 57 | plot(x',y) 58 | xlabel("Connecting Distance") 59 | ylabel("Path Length") -------------------------------------------------------------------------------- /升级后的改进A星算法/寻优/对r进行寻优/ASTARPATH.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JackJu-HIT/Path-Planning-Algorithm/ce441c109c10f1015d969231fcd335090c442367/升级后的改进A星算法/寻优/对r进行寻优/ASTARPATH.m -------------------------------------------------------------------------------- /升级后的改进A星算法/寻优/对r进行寻优/ASTARPATH_improved.m: -------------------------------------------------------------------------------- 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9 | 115.56 10 | 115.56 11 | 115.56 12 | 115.56 13 | 115.56 14 | 115.56 15 | 115.56 16 | 115.56 17 | 115.56 18 | 115.56 19 | 115.56 20 | 115.56 21 | 115.56 22 | 115.56 23 | 115.56 24 | 115.56 25 | 115.56 26 | 115.56 27 | 115.56 28 | 115.56 29 | 115.56 30 | 115.56 31 | 115.56 32 | 115.56 33 | 115.56 34 | 115.56 35 | 115.56 36 | 115.56 37 | 115.56 38 | 115.56 39 | 115.56 40 | 115.56 41 | 115.97 42 | 115.76 43 | 115.76 44 | 115.76 45 | 115.76 46 | 115.76 47 | 115.76 48 | 115.76 49 | 115.76 50 | 115.76 51 | 115.76 52 | 115.76 53 | 115.76 54 | 115.76 55 | 115.76 56 | 115.76 57 | 115.76 58 | 115.76 59 | 115.76 60 | 115.76 61 | 115.76 62 | 115.76 63 | 115.76 64 | 115.76 65 | 115.76 66 | 115.76 67 | 115.76 68 | 115.76 69 | 115.76 70 | 115.76 71 | 115.76 72 | 115.76 73 | 115.76 74 | 115.76 75 | 115.76 76 | 115.76 77 | 115.76 78 | 115.76 79 | 115.76 80 | 115.76 81 | 115.76 82 | 115.76 83 | 115.76 84 | 115.76 85 | 115.76 86 | 115.76 87 | 115.76 88 | 115.76 89 | 115.76 90 | 115.76 91 | 115.76 92 | 115.76 93 | 115.76 94 | 115.76 95 | 115.76 96 | 115.76 97 | 115.76 98 | 115.76 99 | 115.76 100 | 115.76 101 | 115.76 102 | 115.76 103 | 115.76 104 | 115.67 105 | 115.67 106 | 115.67 107 | 115.67 108 | 115.67 109 | 115.67 110 | 115.67 111 | 115.67 112 | 115.67 113 | 115.67 114 | 115.67 115 | 115.67 116 | 115.67 117 | 115.67 118 | 115.67 119 | 115.67 120 | 115.67 121 | 115.67 122 | 115.67 123 | 115.67 124 | 115.67 125 | 115.67 126 | 115.67 127 | 115.67 128 | 115.67 129 | 115.67 130 | 115.67 131 | 115.67 132 | 115.67 133 | 115.67 134 | 115.67 135 | 115.67 136 | 115.67 137 | 115.67 138 | 115.67 139 | 115.67 140 | 115.67 141 | 115.67 142 | 115.67 143 | 115.67 144 | 115.67 145 | 115.67 146 | 115.67 147 | 115.67 148 | 115.67 149 | 115.67 150 | 115.67 151 | 115.67 152 | 115.67 153 | 115.67 154 | 115.67 155 | 115.67 156 | 115.67 157 | 115.67 158 | 115.67 159 | 115.67 160 | 115.67 161 | 115.67 162 | 115.67 163 | 115.67 164 | 115.67 165 | 115.67 166 | 115.67 167 | 115.67 168 | 115.67 169 | 115.67 170 | 115.67 171 | 115.67 172 | 115.67 173 | 115.67 174 | 115.67 175 | 115.67 176 | 115.67 177 | 115.67 178 | 115.67 179 | 115.67 180 | 115.67 181 | 115.67 182 | 115.67 183 | 115.67 184 | 115.67 185 | 115.67 186 | 115.67 187 | 115.67 188 | 115.67 189 | 115.67 190 | 115.67 191 | 117.34 192 | 120.38 193 | 120.38 194 | 120.38 195 | 120.38 196 | 120.38 197 | 120.38 198 | 120.38 199 | 120.38 200 | 120.38 201 | 120.38 202 | 120.38 203 | 120.38 204 | 120.38 205 | 120.38 206 | 120.38 207 | 120.38 208 | 120.38 209 | 120.38 210 | 120.38 211 | 120.38 212 | 120.38 213 | 120.38 214 | 120.38 215 | 120.38 216 | 120.38 217 | 120.38 218 | 120.38 219 | 120.38 220 | 120.38 221 | 120.38 222 | 120.38 223 | 120.38 224 | 120.38 225 | 120.38 226 | 120.38 227 | 120.38 228 | 120.38 229 | 120.38 230 | 120.38 231 | 120.38 232 | 120.38 233 | 120.38 234 | 120.38 235 | 120.38 236 | 120.38 237 | 120.38 238 | 120.38 239 | 120.38 240 | 120.38 241 | 120.38 242 | 120.38 243 | 120.38 244 | 120.38 245 | 120.38 246 | 120.38 247 | 120.38 248 | 120.38 249 | 120.38 250 | 120.38 251 | 120.38 252 | 120.38 253 | 120.38 254 | 120.38 255 | 120.38 256 | 120.38 257 | 120.38 258 | 120.38 259 | 120.38 260 | 120.38 261 | 120.38 262 | 120.38 263 | 120.38 264 | 120.38 265 | 120.38 266 | 120.38 267 | 120.38 268 | 120.38 269 | 120.38 270 | 120.38 271 | 120.38 272 | 120.38 273 | 120.38 274 | 120.38 275 | 120.38 276 | 120.38 277 | 120.38 278 | 120.38 279 | 120.38 280 | 120.38 281 | 120.38 282 | 120.38 283 | 120.38 284 | 120.38 285 | 120.38 286 | 120.38 287 | 120.38 288 | 120.38 289 | 120.38 290 | 120.38 291 | 120.38 292 | 120.38 293 | 120.38 294 | 120.38 295 | 120.38 296 | 120.38 297 | 120.38 298 | 120.38 299 | 120.38 300 | 120.38 301 | 120.38 302 | 120.38 303 | 120.38 304 | 123.6 305 | 123.6 306 | 123.6 307 | 123.6 308 | 123.6 309 | 123.6 310 | 123.6 311 | 123.6 312 | 123.6 313 | 123.6 314 | 123.6 315 | 123.6 316 | 123.6 317 | 123.6 318 | 123.6 319 | 123.6 320 | 123.6 321 | 123.6 322 | 123.6 323 | 123.6 324 | 123.6 325 | 123.6 326 | 123.6 327 | 123.6 328 | 123.6 329 | 123.6 330 | 123.6 331 | 123.6 332 | 123.6 333 | 123.6 334 | 123.6 335 | 123.6 336 | 123.6 337 | 123.6 338 | 123.6 339 | 123.6 340 | 123.6 341 | 123.6 342 | 123.6 343 | 123.6 344 | 123.6 345 | 123.6 346 | 123.6 347 | 123.6 348 | 123.6 349 | 123.6 350 | 123.6 351 | 123.6 352 | 123.6 353 | 123.6 354 | 123.6 355 | 123.6 356 | 123.6 357 | 123.6 358 | 123.6 359 | 123.6 360 | 123.6 361 | 123.6 362 | 123.6 363 | 123.6 364 | 123.6 365 | 123.6 366 | 123.6 367 | 123.6 368 | 123.6 369 | 123.6 370 | 123.6 371 | 123.6 372 | 123.6 373 | 123.6 374 | 123.6 375 | 123.6 376 | 123.6 377 | 123.6 378 | 123.6 379 | 123.6 380 | 123.6 381 | 123.6 382 | 123.6 383 | 123.6 384 | 123.6 385 | 123.6 386 | 123.6 387 | 123.6 388 | 123.6 389 | 123.6 390 | 123.6 391 | 123.6 392 | 123.6 393 | 123.6 394 | 123.6 395 | 123.6 396 | 123.6 397 | 123.6 398 | 123.6 399 | 123.6 400 | 123.6 401 | 123.6 402 | 123.6 403 | 123.6 404 | 123.6 405 | 123.6 406 | 123.6 407 | 123.6 408 | 123.6 409 | 123.6 410 | 123.6 411 | 123.6 412 | 123.6 413 | 123.6 414 | 123.6 415 | 123.6 416 | 123.6 417 | 123.6 418 | 123.6 419 | 123.6 420 | 123.6 421 | 123.6 422 | 123.6 423 | 123.6 424 | 123.6 425 | 123.6 426 | 123.6 427 | 123.6 428 | 123.6 429 | 123.6 430 | 123.6 431 | 123.6 432 | 123.6 433 | 123.6 434 | 123.6 435 | 123.6 436 | 123.6 437 | 123.6 438 | 123.6 439 | 123.6 440 | 123.6 441 | 126.7 442 | 130.06 443 | 130.06 444 | 130.06 445 | 130.06 446 | 130.06 447 | 130.06 448 | 130.06 449 | 130.06 450 | 130.06 451 | 130.06 452 | 130.06 453 | 130.06 454 | 130.06 455 | 130.06 456 | 130.06 457 | 130.06 458 | 130.06 459 | 130.06 460 | 130.06 461 | 130.06 462 | 130.06 463 | 130.06 464 | 130.06 465 | 130.06 466 | 130.06 467 | 130.06 468 | 130.06 469 | 130.06 470 | 130.06 471 | 130.06 472 | 130.06 473 | 130.06 474 | 130.06 475 | 130.06 476 | 130.06 477 | 130.06 478 | 130.06 479 | 130.06 480 | 130.06 481 | 130.06 482 | 130.06 483 | 130.06 484 | 130.06 485 | 130.06 486 | 130.06 487 | 130.06 488 | 130.06 489 | 130.06 490 | 130.06 491 | 130.06 492 | ]; 493 | x=[]; 494 | for i=1:0.1:50 495 | x=[x i]; 496 | end 497 | plot(x',y) 498 | xlabel("r") 499 | ylabel("Path Length") -------------------------------------------------------------------------------- /升级后的改进A星算法/寻优/寻优结果.docx: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JackJu-HIT/Path-Planning-Algorithm/ce441c109c10f1015d969231fcd335090c442367/升级后的改进A星算法/寻优/寻优结果.docx -------------------------------------------------------------------------------- /升级后的改进A星算法/改进A_算法在AGV小车路径规划中的应用_王立春/对比2/ASTARPATH.m: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/JackJu-HIT/Path-Planning-Algorithm/ce441c109c10f1015d969231fcd335090c442367/升级后的改进A星算法/改进A_算法在AGV小车路径规划中的应用_王立春/对比2/ASTARPATH.m -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- 1 | 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 115.76 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119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.07 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 119.9 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5 | 130.06 6 | -------------------------------------------------------------------------------- /升级后的改进A星算法/数据/对r寻优数据结果/1.txt: -------------------------------------------------------------------------------- 1 | 117.27 2 | 115.56 3 | 115.56 4 | 115.56 5 | 115.97 6 | 115.76 7 | 115.76 8 | 115.76 9 | 115.76 10 | 115.76 11 | 115.76 12 | 115.67 13 | 115.67 14 | 115.67 15 | 115.67 16 | 115.67 17 | 115.67 18 | 115.67 19 | 115.67 20 | 117.34 21 | 120.38 22 | 120.38 23 | 120.38 24 | 120.38 25 | 120.38 26 | 120.38 27 | 120.38 28 | 120.38 29 | 120.38 30 | 120.38 31 | 120.38 32 | 123.6 33 | 123.6 34 | 123.6 35 | 123.6 36 | 123.6 37 | 123.6 38 | 123.6 39 | 123.6 40 | 123.6 41 | 123.6 42 | 123.6 43 | 123.6 44 | 123.6 45 | 126.7 46 | 130.06 47 | 130.06 48 | 130.06 49 | 130.06 50 | 130.06 51 | -------------------------------------------------------------------------------- /升级后的改进A星算法/数据/对r寻优数据结果/IM_R_LENGTH.txt: -------------------------------------------------------------------------------- 1 | 117.27 2 | 117.27 3 | 117.27 4 | 115.56 5 | 115.56 6 | 115.56 7 | 115.56 8 | 115.56 9 | 115.56 10 | 115.56 11 | 115.56 12 | 115.56 13 | 115.56 14 | 115.56 15 | 115.56 16 | 115.56 17 | 115.56 18 | 115.56 19 | 115.56 20 | 115.56 21 | 115.56 22 | 115.56 23 | 115.56 24 | 115.56 25 | 115.56 26 | 115.56 27 | 115.56 28 | 115.56 29 | 115.56 30 | 115.56 31 | 115.56 32 | 115.56 33 | 115.56 34 | 115.56 35 | 115.56 36 | 115.56 37 | 115.56 38 | 115.56 39 | 115.56 40 | 115.56 41 | 115.97 42 | 115.76 43 | 115.76 44 | 115.76 45 | 115.76 46 | 115.76 47 | 115.76 48 | 115.76 49 | 115.76 50 | 115.76 51 | 115.76 52 | 115.76 53 | 115.76 54 | 115.76 55 | 115.76 56 | 115.76 57 | 115.76 58 | 115.76 59 | 115.76 60 | 115.76 61 | 115.76 62 | 115.76 63 | 115.76 64 | 115.76 65 | 115.76 66 | 115.76 67 | 115.76 68 | 115.76 69 | 115.76 70 | 115.76 71 | 115.76 72 | 115.76 73 | 115.76 74 | 115.76 75 | 115.76 76 | 115.76 77 | 115.76 78 | 115.76 79 | 115.76 80 | 115.76 81 | 115.76 82 | 115.76 83 | 115.76 84 | 115.76 85 | 115.76 86 | 115.76 87 | 115.76 88 | 115.76 89 | 115.76 90 | 115.76 91 | 115.76 92 | 115.76 93 | 115.76 94 | 115.76 95 | 115.76 96 | 115.76 97 | 115.76 98 | 115.76 99 | 115.76 100 | 115.76 101 | 115.76 102 | 115.76 103 | 115.76 104 | 115.67 105 | 115.67 106 | 115.67 107 | 115.67 108 | 115.67 109 | 115.67 110 | 115.67 111 | 115.67 112 | 115.67 113 | 115.67 114 | 115.67 115 | 115.67 116 | 115.67 117 | 115.67 118 | 115.67 119 | 115.67 120 | 115.67 121 | 115.67 122 | 115.67 123 | 115.67 124 | 115.67 125 | 115.67 126 | 115.67 127 | 115.67 128 | 115.67 129 | 115.67 130 | 115.67 131 | 115.67 132 | 115.67 133 | 115.67 134 | 115.67 135 | 115.67 136 | 115.67 137 | 115.67 138 | 115.67 139 | 115.67 140 | 115.67 141 | 115.67 142 | 115.67 143 | 115.67 144 | 115.67 145 | 115.67 146 | 115.67 147 | 115.67 148 | 115.67 149 | 115.67 150 | 115.67 151 | 115.67 152 | 115.67 153 | 115.67 154 | 115.67 155 | 115.67 156 | 115.67 157 | 115.67 158 | 115.67 159 | 115.67 160 | 115.67 161 | 115.67 162 | 115.67 163 | 115.67 164 | 115.67 165 | 115.67 166 | 115.67 167 | 115.67 168 | 115.67 169 | 115.67 170 | 115.67 171 | 115.67 172 | 115.67 173 | 115.67 174 | 115.67 175 | 115.67 176 | 115.67 177 | 115.67 178 | 115.67 179 | 115.67 180 | 115.67 181 | 115.67 182 | 115.67 183 | 115.67 184 | 115.67 185 | 115.67 186 | 115.67 187 | 115.67 188 | 115.67 189 | 115.67 190 | 115.67 191 | 117.34 192 | 120.38 193 | 120.38 194 | 120.38 195 | 120.38 196 | 120.38 197 | 120.38 198 | 120.38 199 | 120.38 200 | 120.38 201 | 120.38 202 | 120.38 203 | 120.38 204 | 120.38 205 | 120.38 206 | 120.38 207 | 120.38 208 | 120.38 209 | 120.38 210 | 120.38 211 | 120.38 212 | 120.38 213 | 120.38 214 | 120.38 215 | 120.38 216 | 120.38 217 | 120.38 218 | 120.38 219 | 120.38 220 | 120.38 221 | 120.38 222 | 120.38 223 | 120.38 224 | 120.38 225 | 120.38 226 | 120.38 227 | 120.38 228 | 120.38 229 | 120.38 230 | 120.38 231 | 120.38 232 | 120.38 233 | 120.38 234 | 120.38 235 | 120.38 236 | 120.38 237 | 120.38 238 | 120.38 239 | 120.38 240 | 120.38 241 | 120.38 242 | 120.38 243 | 120.38 244 | 120.38 245 | 120.38 246 | 120.38 247 | 120.38 248 | 120.38 249 | 120.38 250 | 120.38 251 | 120.38 252 | 120.38 253 | 120.38 254 | 120.38 255 | 120.38 256 | 120.38 257 | 120.38 258 | 120.38 259 | 120.38 260 | 120.38 261 | 120.38 262 | 120.38 263 | 120.38 264 | 120.38 265 | 120.38 266 | 120.38 267 | 120.38 268 | 120.38 269 | 120.38 270 | 120.38 271 | 120.38 272 | 120.38 273 | 120.38 274 | 120.38 275 | 120.38 276 | 120.38 277 | 120.38 278 | 120.38 279 | 120.38 280 | 120.38 281 | 120.38 282 | 120.38 283 | 120.38 284 | 120.38 285 | 120.38 286 | 120.38 287 | 120.38 288 | 120.38 289 | 120.38 290 | 120.38 291 | 120.38 292 | 120.38 293 | 120.38 294 | 120.38 295 | 120.38 296 | 120.38 297 | 120.38 298 | 120.38 299 | 120.38 300 | 120.38 301 | 120.38 302 | 120.38 303 | 120.38 304 | 123.6 305 | 123.6 306 | 123.6 307 | 123.6 308 | 123.6 309 | 123.6 310 | 123.6 311 | 123.6 312 | 123.6 313 | 123.6 314 | 123.6 315 | 123.6 316 | 123.6 317 | 123.6 318 | 123.6 319 | 123.6 320 | 123.6 321 | 123.6 322 | 123.6 323 | 123.6 324 | 123.6 325 | 123.6 326 | 123.6 327 | 123.6 328 | 123.6 329 | 123.6 330 | 123.6 331 | 123.6 332 | 123.6 333 | 123.6 334 | 123.6 335 | 123.6 336 | 123.6 337 | 123.6 338 | 123.6 339 | 123.6 340 | 123.6 341 | 123.6 342 | 123.6 343 | 123.6 344 | 123.6 345 | 123.6 346 | 123.6 347 | 123.6 348 | 123.6 349 | 123.6 350 | 123.6 351 | 123.6 352 | 123.6 353 | 123.6 354 | 123.6 355 | 123.6 356 | 123.6 357 | 123.6 358 | 123.6 359 | 123.6 360 | 123.6 361 | 123.6 362 | 123.6 363 | 123.6 364 | 123.6 365 | 123.6 366 | 123.6 367 | 123.6 368 | 123.6 369 | 123.6 370 | 123.6 371 | 123.6 372 | 123.6 373 | 123.6 374 | 123.6 375 | 123.6 376 | 123.6 377 | 123.6 378 | 123.6 379 | 123.6 380 | 123.6 381 | 123.6 382 | 123.6 383 | 123.6 384 | 123.6 385 | 123.6 386 | 123.6 387 | 123.6 388 | 123.6 389 | 123.6 390 | 123.6 391 | 123.6 392 | 123.6 393 | 123.6 394 | 123.6 395 | 123.6 396 | 123.6 397 | 123.6 398 | 123.6 399 | 123.6 400 | 123.6 401 | 123.6 402 | 123.6 403 | 123.6 404 | 123.6 405 | 123.6 406 | 123.6 407 | 123.6 408 | 123.6 409 | 123.6 410 | 123.6 411 | 123.6 412 | 123.6 413 | 123.6 414 | 123.6 415 | 123.6 416 | 123.6 417 | 123.6 418 | 123.6 419 | 123.6 420 | 123.6 421 | 123.6 422 | 123.6 423 | 123.6 424 | 123.6 425 | 123.6 426 | 123.6 427 | 123.6 428 | 123.6 429 | 123.6 430 | 123.6 431 | 123.6 432 | 123.6 433 | 123.6 434 | 123.6 435 | 123.6 436 | 123.6 437 | 123.6 438 | 123.6 439 | 123.6 440 | 123.6 441 | 126.7 442 | 130.06 443 | 130.06 444 | 130.06 445 | 130.06 446 | 130.06 447 | 130.06 448 | 130.06 449 | 130.06 450 | 130.06 451 | 130.06 452 | 130.06 453 | 130.06 454 | 130.06 455 | 130.06 456 | 130.06 457 | 130.06 458 | 130.06 459 | 130.06 460 | 130.06 461 | 130.06 462 | 130.06 463 | 130.06 464 | 130.06 465 | 130.06 466 | 130.06 467 | 130.06 468 | 130.06 469 | 130.06 470 | 130.06 471 | 130.06 472 | 130.06 473 | 130.06 474 | 130.06 475 | 130.06 476 | 130.06 477 | 130.06 478 | 130.06 479 | 130.06 480 | 130.06 481 | 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